1. Field of Use
These teachings relate generally to a system and method for snow pack monitoring and more particularly to a ground based system that provides real time measurement and collection of snow-water equivalent (SWE) data in remote settings.
2. Description of Prior Art (Background)
The importance of snow water equivalence (SWE) estimation and snowpack studies to science and society is well known. For example, snowpack studies are critical to water planning for agriculture, recreation, cities, military, etc. The importance of snowpack data is emphasized by the fact that SWE data is so fundamental to so many interests that NOAA publishes daily global SWE composites online. As suggested in the literature most snow studies are motivated by the need to estimate basin-wide runoff to provide operational forecasting for snow-affected industries or rivers subject to flooding, or to improve climate forecasting. These applications require knowledge about the spatial distribution of SWE over large spatial scales, often in basins characterized by complex terrain and heterogeneous land cover. Prior research has demonstrated that SWE exhibits extreme variability in space. This variability is a result of influences of and interactions between meteorology (wind speed and direction, radiation), topography (elevation gradients, slope and aspect), and vegetation cover. In temperate landscapes, forest vegetation exerts important controls on snow distribution through its role in intercepting snow, attenuating wind, and altering radiation at the snow surface. Therefore, to obtain accurate SWE profiles of topographically complex areas, especially mountainous terrain, good temporal and spatial resolution is needed, with techniques that are robust to the effects of vegetation cover.
While several approaches exist for measuring and quantitatively characterizing the spatial distribution of SWE, including manual surveys and airborne sensors, continuous, automated ground-based techniques allow for better spatial resolution, more frequent measurement, and are not affected by tree canopies. Emerging approaches exploit various technologies, including gamma ray detection and acoustics, but these are largely still in the development phase and have not been deployed remotely as continuous, unmanned stations. The most prevalent ground-based, continuous method of SWE measurement makes use of snow pillows, which measures now mass by measuring loads on liquid-filled bags (the pillow). In particular, the SNOTEL network run by the NRCS (Natural Resources Conservation Service) relies heavily on snow pillow technology. However, this method is susceptible to the phenomena of snow bridging (a gap forming between the snowpack and the ground). To mitigate (i.e., average out) the snow bridging effect, snow pillows are made large, approximately 50 to 100 square feet on average, and thus contain up to several hundred gallons of liquid, typically antifreeze. This means that snow pillows can only be installed in large, flat areas to accommodate their size, and near roads or well-established trails for transporting the apparatus. Large plate-style snow load sensors with multiple load nodes and associated analytic techniques to correct for snow bridging have been proposed to supplant snow pillows, but this technology is not yet extensively used in practice. The upshot of this is that in addition to inaccuracies due to snow bridging, predominant ground-based SWE measurement technologies do not provide effective resolution at the basin scale, due to their expense and difficulty of deployment.
In the SNOTEL network this is exacerbated by the general reliance on traditional data logging platforms such as the Campbell Scientific CR1000, which are heavy, expensive, and power hungry. Indeed, most long-term continuously monitoring snow stations are located in clearings, representing a biased measurement of SWE. Thus, developing a system to cost effectively collect data to improve the spatial and temporal resolution of SWE monitoring would have important impacts on both the scientific study of snow and social infrastructures dependent on snow. Furthermore, a system that is compact and portable would have the advantage of being deployable in variety of settings including those presently not observable with remote sensing technologies (e.g., snow packs under forest canopies or on sloped terrain). A low cost SWE data collection system would allow more data points to be measured, further improving spatial resolution, and would bring this technology to a broader user base.
In light of the above, there exists a need for a relatively inexpensive and easily deployable sensor platform that can be wirelessly networked, remotely accessed, and is robust to harsh winter environments.
There is also a need for a novel SWE measurement instrument suite and control algorithms suitable for integration with a relatively inexpensive and easily deployable sensor platform that can be wirelessly networked, remotely accessed, and is robust to harsh winter environments.